In [ ]:
import pandas as pd
import seaborn as sns
import plotly.express as px
import numpy as np

import matplotlib.pyplot as plt
In [ ]:
import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"

Matplotlib¶

For this excercise, we have written the following code to load the stock dataset built into plotly express.

In [ ]:
stocks = px.data.stocks()
stocks.head()
Out[ ]:
date GOOG AAPL AMZN FB NFLX MSFT
0 2018-01-01 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
1 2018-01-08 1.018172 1.011943 1.061881 0.959968 1.053526 1.015988
2 2018-01-15 1.032008 1.019771 1.053240 0.970243 1.049860 1.020524
3 2018-01-22 1.066783 0.980057 1.140676 1.016858 1.307681 1.066561
4 2018-01-29 1.008773 0.917143 1.163374 1.018357 1.273537 1.040708

Question 1:¶

Select a stock and create a suitable plot for it. Make sure the plot is readable with relevant information, such as date, values.

In [ ]:
# YOUR CODE HERE

fig, ax = plt.subplots()

ax.plot(stocks.loc[:,'date'],stocks.loc[:,'NFLX'], 'r')
ax.set_xticks(stocks.loc[::10,'date'])

#make the figure longer
fig.set_figwidth(10)
#display date vertically
plt.xticks(rotation=90)
# set title
ax.set_title('Evolution of Netflix stocks')
# horizontal axis
ax.set_xlabel('Date')
# vertical axis
ax.set_ylabel('Stock value (% of initial value)')
plt.show()

Question 2:¶

You've already plot data from one stock. It is possible to plot multiples of them to support comparison.
To highlight different lines, customise line styles, markers, colors and include a legend to the plot.

In [ ]:
# YOUR CODE HERE
fig, ax = plt.subplots()

ax.plot(stocks.loc[:,'date'],stocks.loc[:,'GOOG'], 'c', label='GOOG')
ax.plot(stocks.loc[:,'date'],stocks.loc[:,'AAPL'], 'grey', label='AAPL')
ax.plot(stocks.loc[:,'date'],stocks.loc[:,'AMZN'], 'y', label='AMZN')
ax.plot(stocks.loc[:,'date'],stocks.loc[:,'FB'], 'b', label='FB')
ax.plot(stocks.loc[:,'date'],stocks.loc[:,'NFLX'], 'r', label='NFLX')
ax.plot(stocks.loc[:,'date'],stocks.loc[:,'MSFT'], 'g', label='MSFT')

ax.set_xticks(stocks.loc[::10,'date'])

#make the figure longer
fig.set_figwidth(10)
#display date vertically
plt.xticks(rotation=90)

# set title
ax.set_title('Evolution of GAFAM + Netflix stocks')
# horizontal axis
ax.set_xlabel('Date')
# vertical axis
ax.set_ylabel('Stock value (% of initial value)')
#legend
plt.legend()
plt.show()

Seaborn¶

First, load the tips dataset

In [ ]:
tips = sns.load_dataset('tips')
tips.head()
Out[ ]:
total_bill tip sex smoker day time size
0 16.99 1.01 Female No Sun Dinner 2
1 10.34 1.66 Male No Sun Dinner 3
2 21.01 3.50 Male No Sun Dinner 3
3 23.68 3.31 Male No Sun Dinner 2
4 24.59 3.61 Female No Sun Dinner 4

Question 3:¶

Let's explore this dataset. Pose a question and create a plot that support drawing answers for your question.

Some possible questions:

  • Are there differences between male and female when it comes to giving tips?
  • What attribute correlate the most with tip?
In [ ]:
# YOUR CODE HERE

# QUESTION : Which day of the week is the most profitable tip-wise ?
# ANSWER : Show the tip repartition by day 

fig, ax = plt.subplots()
sns.boxplot(x='day', y='tip', data=tips, showmeans=True)
ax.set_ylabel('Tip (in euro)')
plt.show()

Plotly Express¶

Question 4:¶

Redo the above exercises (challenges 2 & 3) with plotly express. Create diagrams which you can interact with.

The stocks dataset¶

Hints:

  • Turn stocks dataframe into a structure that can be picked up easily with plotly express
In [ ]:
# YOUR CODE HERE
fig = px.line(stocks, x="date", y=['GOOG', 'AAPL', 'AMZN', 'FB', 'MSFT', 'NFLX'], markers=True)
fig.show()

The tips dataset¶

In [ ]:
# YOUR CODE HERE
fig = px.box(tips, x='day', y='tip')
fig.show()

Question 5:¶

Recreate the barplot below that shows the population of different continents for the year 2007.

Hints:

  • Extract the 2007 year data from the dataframe. You have to process the data accordingly
  • use plotly bar
  • Add different colors for different continents
  • Sort the order of the continent for the visualisation. Use axis layout setting
  • Add text to each bar that represents the population
In [ ]:
#load data
df = px.data.gapminder()
df.head()
Out[ ]:
country continent year lifeExp pop gdpPercap iso_alpha iso_num
0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG 4
1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG 4
2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG 4
3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG 4
4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG 4
In [ ]:
# YOUR CODE HERE
df_2007 = df.query('year==2007')
df_2007_new = df_2007.groupby('continent').sum()
df_2007_new = df_2007_new.reset_index()


fig = px.bar(df_2007_new, x="pop", y='continent', orientation='h', color = 'continent', text = 'pop')
fig.update_yaxes(categoryorder="min ascending")
fig.show()
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